The People’s Republic of China is the world's largest emitter of greenhouse gases (GHGs), 44% (4.14 billion metric tons of CO2) of which came from the electric power system in 2017.1 According to its Nationally Determined Contribution to the United Nations Framework Convention on Climate Change, the Chinese government pledged to lower CO2 emissions per unit of GDP by 60%–65% relative to 2005 emissions, increasing the share of non-fossil fuels in primary energy consumption to around 20%2 by 2030. Accelerating the replacement of traditional energy with renewable substitutes would help transform the power system and reduce carbon emissions. China has already demonstrated rapid growth in renewable electricity generation, which increased from 736 TWh (17% of total electricity generation) to 1,931 TWh (26%) between 2010 and 2019.3
Renewable energy costs have experienced an unprecedented decline. Over the past decade, the global weighted-average levelized cost of electricity has fallen by 82% for photovoltaics (PV), 38% for onshore wind, and 84% for battery storage.4-7 Even without accounting for the social cost of carbon, all-inclusive costs of electricity from new PV and onshore wind are now below the marginal operating costs of an increasing number of existing coal-fired powerplants.5,6 In market economies, this crossover cost trend will support autonomous power system transformation, reduce fossil fuel dependence and related environmental impacts,8-10 and reduce wholesale and retail electricity costs.11 As a result, new opportunities will arise for cutting consumer and manufacturer energy costs, improving human welfare, and boosting economic growth.
The computable general equilibrium (CGE) model is an important tool for studying these macroeconomic impacts. The progress of energy technology in the CGE model is generally characterized by homogeneous autonomous energy efficiency improvement (AEEI) across the economy12,13 or average AEEIs for different energy-consuming sectors,14,15 but neither approach distinguishes technological improvement for different energy sources. Huang et al. (2019) use an exogenously specified mix of fossil and renewable energy as a baseline to describe the process of renewable energy substitution along China’s future low-carbon development pathway.16 However, the exogenous generation mix does not capture the recent cost crossover—with the cost of renewables dropping below the cost of coal power—nor does it evaluate the full economic impacts of technology improvement. Therefore, there is a need for studies that consider both the plummeting renewable energy costs and the associated economic and social impacts.
Carbon emission trading scheme (ETS) is among the most potentially impactful approaches to driving the renewable transition. In 2011, China launched a pilot ETS covering seven leading provinces and cities including Beijing, Shanghai, Guangdong, Shenzhen, Tianjin, Chongqing, and Hubei. By the middle of 2019, the cumulative trading volume of the pilot ETS covered 330 million metric tons of CO2 equivalent emissions, with a cumulative transaction value of 7.11 billion RMB.17 In December 2017, China officially launched its national ETS, and electric power was the first industry included in the system.18
To date, the research literature suggests that China’s ETS mitigates CO2 emissions and promotes renewable energy deployment but hinders the economy—although there is significant disagreement about the magnitude and direction of net economic impacts. Chen et al. (2020) find that a 1% carbon emission reduction would reduce GDP by 0.06%,19 whereas Zhang et al. (2018) indicate that the ETS could reduce carbon intensity by 20% without GDP loss.20 Most studies show a negative net impact on the economy due to the ETS, ranging from 0.08% to 5.61% of real GDP12,21 and depending on carbon pricing,22 the allowance allocation mechanism,15,23-25 revenue redistribution methods,16,26 government fines,27 and coverage considered.13,14,28 The price-directed nature of the ETS still confers efficiency gains compared with mandated emission caps.29,30 However, few studies focus on the comprehensive impact of renewable technology promotion and the ETS in the context of the renewable energy cost crossover.
Further, the existing literature does not account for three critical multiplier effects that could magnify the impacts of accelerating China’s renewable electricity transition through technology improvement and the ETS. First, households can be expected to divert the electricity cost savings that result from rapidly declining renewable energy costs toward other expenditures, promoting broad-based economic growth from the demand side. Second, because China generally has a higher elasticity of labor supply compared with developed countries, this more accommodating labor supply will amplify economic gains due to the expenditure stimulus, particularly in the long term. Third, increasing renewable energy use may increase regional total factor productivity (TFP), supporting the anecdotal narrative of a virtuous productivity cycle linking renewable energy deployment to other technological innovation; although the exact mechanisms are difficult to disentangle, studies in diverse settings suggest a positive linkage.31-33 Tugcu (2013) estimates long-run (short-run) growth elasticities are -2.1 (-1.7) and 0.8 (0.7) for fossil and renewable consumption, respectively.34 Yan et al. (2020) study the relationship between renewable energy technology innovations and China’s green productivity growth, finding a significant effect in provinces where income is above a certain threshold.35
Ours is the first study to account for the economic impacts of shifting consumption patterns, job growth in the context of elastic labor supply, and higher TFP when simulating accelerated renewable electricity growth with the ETS in China. We employ a dynamic recursive CGE model with a diverse portfolio of electric power technologies to evaluate scenarios designed for illuminating each of the three multiplier effects. Those scenarios are described in the next section, followed by our results and conclusions. The Methods section details our analytical approach.
Scenarios Evaluated
We evaluate six scenarios (Table 1). In the business as usual (BAU) scenario, the productivity of renewables (wind, solar, and electricity storage) is assumed to follow historical trends (He et al. 2020),11 with sustained but moderate cost reductions into the future. In the low-cost renewable scenario (R), more rapid productivity growth in renewables continues in alignment with recent indications, simulating how lower renewable energy costs provide direct stimulus to the economy. In the carbon constraint scenario (C50), total emissions from the power sector by 2030 are limited to 50% below the 2015 level by instituting an ETS for this sector.
Table 1: Scenarios Evaluated
Scenarios
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Description
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BAU
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Moderate productivity improvements in renewables
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R
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Rapid productivity improvements in renewables
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C50
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R + ETS limiting CO2 emissions from the power sector to 50% of the 2015 emissions level
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Keynes
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C50 + shifting expenditure from energy savings
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EMP
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Keynes + more accommodating labor market
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PROD
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EMP + energy productivity spillovers (1% TFP growth)
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Our other three scenarios examine the multiplier economic impacts of renewable innovation and the ETS. The Keynes scenario builds on the C50 scenario to elucidate the effects of demand-side aggregate stimulus resulting from energy cost savings due to renewable technology innovation. Previous economic assessments of renewable energy have focused on three component impacts: investment in technology production, technology purchasing, and installation. Technology production (e.g., building and operating a solar panel factory) represents so-called “shovel ready” investment and is usually an unambiguous economic stimulus. Technology purchase and installation costs can have mixed economic effects, depending on the opportunity cost or alternative return to capital. In other words, technology adoption will stimulate the economy if it increases productivity, reduces resource costs, or both. If it reduces productivity or increases resource costs, it will be detrimental to growth.
The shifting consumption patterns in the Keynes scenario begin with lower-cost electricity saving money for households and enterprises. These savings are diverted toward other expenditures, mostly domestic services that employ workers from all skill levels and demographics and that are non-tradable, meaning these new jobs cannot be outsourced. Because more than half of China's aggregate domestic demand is from household consumption and 70% of this goes to services, about half of energy cost savings diverted to other expenditures can be expected to go to this employment category—the most labor intensive and skill diverse in the economy. In contrast, the carbon fuel supply chain is among the least employment intensive; for example, the carbon fuel supply chain produces only 1%–10% as many jobs per unit of revenue compared with the service sector, differences far too large to be offset by wage inequality (Figure 1). Simply put, about two thirds of the money saved on cheaper electricity is spent on services, stimulating much stronger and more diverse domestic job growth.
The EMP scenario builds on the Keynes scenario to examine the role of labor supply in economic adjustment. Based on China’s dynamism over most of the last two decades, the BAU, R, C50, and Keynes scenarios assume relatively “full employment,” which would limit the economy’s supply response to positive or negative stimulus. China’s reform period has seen dramatic economic growth and large-scale labor mobilization, but over the last decade growth and rates of job creation have moderated (particularly in the post-COVID economy). To reflect this, the EMP scenario assumes a low but nonzero elasticity of aggregate labor supply (0.25), describing a more accommodating labor market.
Finally, the PROD scenario builds on the EMP scenario to exemplify renewable energy productivity spillovers, assuming that accelerated renewable deployment confers an average of 1% higher TFP growth on the economy. This value is not a forecast; it merely illustrates the growth potential of induced innovation.